similar to: ggplot2: stat_smooth for family=binomial with cbind(Y, N) formula

In the example below, from http://www.ling.upenn.edu/~joseff/rstudy/summer2010_ggplot2_intro.html I'd like to make (a) the fitted line thicker and (b) change the background fill color for the confidence envelope around each fitted line to a low-alpha transparent version of the same color used for the separate fitted lines for GENDER, rather than grey for both. How can I do this?

Hi all, Does one of you know if there is any way to combine a "nls" method in the stat_smooth of ggplot? Regards -- ------------- Benoit Boulinguiez Ph.D student Ecole de Chimie de Rennes (ENSCR) Bureau 1.20 Equipe CIP UMR CNRS 6226 "Sciences Chimiques de Rennes" Avenue du G?n?ral Leclerc CS 50837 35708 Rennes CEDEX 7 Tel 33 (0)2 23 23 80 83 Fax 33 (0)2 23 23 81 20

I'm interested in creating a graphic -like- this: c <- ggplot(mtcars, aes(qsec, wt)) c + geom_point() + stat_smooth(fill="blue", colour="darkblue", size=2, alpha = 0.2) but I need to show 2 sets of bands (with different shading) using 5%, 25%, 75%, 95% limits that I specify and where the heavy blue line is the median. I don't understand how to do this with

Dear all I would like to superpose some smoothing line through boxplot in ggplot > dput(ad) structure(list(konc.f = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 7L, 7L, 7L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,

Hello R folks, I have three questions. I am trying to run a logistic regression (binomial family) where the response variable is a proportion. According to R Documentation in "a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes." However when I run my code I get the following error message: Error in

Suppose that I'm working on Hadley's diamond dataset and I want to review the relationship between price, colour and carat. I might run the following: library(ggplot2) #plot scatter and add some hex binning q<-qplot(carat,price,data=diamonds, geom=c("hex"), main="Variability of Diamond Prices by Carat and Colour") #facet to get one scatter for

This is what I have now so far. p=ggplot(data = test, aes(x = YEAR, y = TOTAL, colour = TREATMENT)) + geom_point() + geom_smooth(method = "lm", se=FALSE) + facet_wrap(~COUNTRY) > p +scale_x_continuous(limits=c(1,4)) http://r.789695.n4.nabble.com/file/n3626510/graph.gif I would also like to: 1.) change the headline for the faced wraps ?high? and ?low?. Is there any other way of

Hello everyone, I was wondering if anyone was aware of a way in which I could use ggplot's stat_smooth() function for add an exponential moving average. I was thinking that I could maybe use something like: >myggplot + stat_smooth (method = 'HoltWinters( data , .9 , 0, 0)') but my efforts were futile. Perhaps there is a way to write my own custom method to throw into

Hi all, I am using ggplot2 and continuing to find it very useful and pretty. However, I am trying to create some graphics for publication that would be included in an MS Word document (not my choice!) in Windows Vista. The problem is that I want to use stat_smooth() to add an fitted linear model line along with its 95% confidence band, but I cannot seem to get the confidence band in a format

Default level = 0.95. Does this mean +/- 0.025 from estimate? [[alternative HTML version deleted]]

Hello, I drew a simple area plot using ggplot2 using set <- read.table(file="http://www.jovian.nl/set.csv", head=1, sep=",") library(ggplot2) ggplot() + layer( data = set, mapping = aes(x = time, y = hours), geom = "area", stat="smooth", color="red" ) + layer( data = set, mapping = aes(x = time, y = hours), geom = "area",

How do I add to a trellis plot the best fit line from a robust fit? I can use panel.lm to add a least squares fit, but there is no panel.rlm function. -- Alan S Barnett <asb at mail.nih.gov> NIMH/CBDB

AF_E PS_E 90.838 2.206 83.139 1.751 134.272 3.710 84.043 2.076 105.184 2.788 157.249 3.783 50.280 1.027 96.973 2.355 123.582 3.398 60.417 1.236 123.501 3.315 90.128 1.566 193.783 5.167 116.036 2.994 100.289 2.216 56.943 1.106 102.272 2.692 145.579 3.810 53.105 1.202 127.212 3.061 102.838 2.383 126.352 2.723 13.661 0.190 164.352 4.870 159.945 4.160 54.382 0.884 128.253 3.598 181.208 4.767 145.118

Hello All, I have plotted a scatter plot in ggplot2 and added a regression line and a regression equation. But the processing is very very slow. One reason might be because I have so many data pairs. Is there any way to speed up this code ? I need to create a multiple layout as well. The code I have used is as follows: setwd("C:/Users/jzd0009/Documents/R software") mydata <-

hi all ,? i have an problem in script R . But when I execute the script R I face this error . can you help me please ???error:----------------------------------------- Error in FUN(X[[i]], ...) :?? Theme element 'text' has NULL property: margin, debugIn addition: Warning messages:1: Removed 361 rows containing non-finite values (stat_smooth).?2: Removed 361 rows containing missing values

Dear all, I have a dataset which I need to estimate the regression model and plot the estimated curve two other curves with low and high confidence interval (CI=95%). How can I do that? x<-1:100 y<-x^0.2+rnorm(100,0.1,0.1) mod<-glm(y~log(x)) plot(y~x) lines(predict(mod)~x,col=2) Kind regards, miltinho Brazil para armazenamento! [[alternative HTML version deleted]]

development.log contains quite a few SELECTS on system tables like COLUMNS. Wouldn''t it be more efficient to cache this info? I''m also wondering about strings like "[4;35;1m". What''s their purpose and is it possible to get rid of them? They look like screen coordinates and makes no sense in a text file. Christer Processing ReportController#create (for

In the change log of ggplot2, version 0.7, I find this: "* scales: any point outside of limits is dropped (this was previously the behaviour for discrete scales, but not continuous scales)" and that makes sense for some applications. But what about if I want to summarize the data with a smooth, and (a) include the effect of all values, but (b) "zoom in" on the smooth by

is it possible to map 2 plots(c and d) in a single output plot? library(ggplot2) c <- ggplot(mtcars, aes(qsec, wt)) d <- ggplot (mtcars, aes(qsec, wt)) c + stat_smooth(fill="darkgrey", colour="blue", size=2, alpha = 0.2) d + stat_smooth(fill="darkgrey", colour="red", size=2, alpha = 0.2) -- View this message in context:

Hello R-users, I've been looking for a function performing one and two sample Hotelling test for testing equality of mean vectors. Has anyone implemented such a function in R? thanks a lot, Bill ============== Bill Donner Statistician